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Background: Preoperative accurate prediction of lymph node status is especially important for the formulation of treatment plans for patients with gastric cancer (GC). The purpose of this study was to establish decision rules and a risk assessment model for lymph node metastasis (LNM) in GC using preoperative indicators. Methods: The clinical data of 554 patients who underwent gastrectomy with D2 lymphadenectomy were collected. A 1:1 propensity score matching (PSM) system was used, and the clinical data of the matched 466 patients were further analyzed. The important risk factors for LNM were extracted by the random forest algorithm, and decision rules and nomogram models for LNM were constructed with a classification tree and the “rms” package of R software, respectively. Results: Tumor size ( OR : 2.058; P = 0.000), computed tomography (CT) findings ( OR : 1.969; P = 0.001), grade ( OR : 0.479; P = 0.000), hemoglobin (Hb) ( OR : 1.211; P = 0.005), CEA ( OR : 1.111; P = 0.017), and CA19-9 ( OR : 1.040; P = 0.033) were independent risk factors for LNM in GC. Tumor size did rank first in the ranking of important factors for LNM in GC and was the first-level segmentation of the two initial branches of the classification tree. The accuracy, sensitivity, specificity, and positive predictive value of the decision rules in diagnosing preoperative LNM in GC were 75.6, 85.7, 73.9, 73.5, and 79.3%, respectively. The accuracy, sensitivity, and specificity of the risk assessment model in predicting preoperative LNM in GC were 79.3, 80.3, and 79.4%, respectively. Conclusion: Tumor size was the most important factor for evaluating LNM in GC. This decision rules and nomogram model constructed to take into account tumor size, CT findings, grade, hemoglobin, CEA, and CA19-9 effectively predicted the incidence of LNM in preoperative GC.

Background: The GINS complex has been implicated in the prognosis of various cancers. It comprises four subunits, encoded by GINS1, GINS2, GINS3, and GINS4 genes. Based on the current understanding, no report exists on the role of the GINS complex in pancreatic cancer. Methods: We employed various bioinformatics databases including GEPIA, UALCAN, GEPIA2, and Kaplan Meier Plotter to identify the expression profile of the four genes (GINS1, GINS2, GINS3, and GINS4), their correlation with pancreatic cancer grade as well as their prognostic value of in pancreatic cancer. Western blotting and qRT-PCR analyses were conducted to verify the expression profiles of the four genes in pancreatic cancer. CCK8 and EdU cell experiments were conducted to reveal the role played by the four genes in pancreatic cancer cell proliferation. Results: Based on GEPIA, Western blotting, and qRT-PCR analyses, all the four genes in the GINS complex were overexpressed in pancreatic cancer. Notably, the expression of each member was significantly associated with pancreatic cancer grade. The prognostic analysis revealed that not only the whole GINS complex but also each individual were prognostic biomarkers for pancreatic cancer. CCK8 and EdU experiments demonstrated that inhibition of the expression of each GINS member lowered pancreatic cancer cell proliferation. Conclusion: This work implicated GINS1, GINS2, GINS3, and GINS4 genes as critical prognostic markers for pancreatic cancer.

Background: Chromobox (CBX) family proteins are a class of transcriptional repressors involved in epigenetic regulation and developmental processes of various tumors, including gastric cancer. However, the function and prognosis of different CBXs in gastric cancer remain unknown. Methods: This study addresses this issue by synthesizing several mainstream databases (Oncomine, GEPIA2, cBioportal, and Kaplan-Meier plotter, among others) that currently contain many tumor samples and provide very reliable analysis results, investigating the role of CBXs in the prognosis of gastric cancer. Results: The mRNA of CBX1/2/3/4/5/8 was highly expressed in gastric cancer, the mRNA of CBX7 was lowly expressed in gastric cancer, and the mRNA expression of CBX6 was not significantly different in CRC. Besides, high and low CBXs mRNA expression correlated with cancer stage, node metastasis status, H. pylori infection status, and tumor grade in CRC patients. We found that high mRNA expression of CBX4/5/6/7/8 was significantly associated with worse overall survival (OS), progression-free survival (FP), and post-progression survival (PPS) in a large number of CRC patients. High mRNA expression of CBX3 was significantly associated with better OS and FP. We also found that none of the eight CBXs family genes had a mutation rate of less than 5% in gastric cancer, and the highest mutation rate was in CBX3 (14%). Conclusions: These results suggest that CBX3/4/5/6/7/8 could be a prognostic biomarker in gastric cancer patients.

BackgroundCD4+ memory T cells are an important component of the tumor microenvironment (TME) and affect tumor occurrence and progression. Nevertheless, there has been no systematic analysis of the effect of CD4+ memory T cells in gastric cancer (GC).MethodsThree datasets obtained from microarray and the corresponding clinical data of GC patients were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. We uploaded the normalize gene expression data with standard annotation to the CIBERSORT web portal for evaluating the proportion of immune cells in the GC samples. The WGCNA was performed to identify the modules the CD4+ memory T cell related module (CD4+ MTRM) which was most significantly associated with CD4+ memory T cell. Univariate Cox analysis was used to screen prognostic CD4+ memory T cell-related genes (CD4+ MTRGs) in CD4+ MTRM. LASSO analysis and multivariate Cox analysis were then performed to construct a prognostic gene signature whose effect was evaluated by Kaplan-Meier curves and receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and decision curve analyses (DCA). A prognostic nomogram was finally established based on the CD4+ MTRGs.ResultWe observed that a high abundance of CD4+ memory T cells was associated with better survival in GC patients. CD4+ MTRM was used to stratify GC patients into three clusters by unsupervised clustering analysis and ten CD4+ MTRGs were identified. Overall survival, five immune checkpoint genes and 17 types of immunocytes were observed to be significantly different among the three clusters. A ten-CD4+ MTRG signature was constructed to predict GC patient prognosis. The ten-CD4+ MTRG signature could divide GC patients into high- and low-risk groups with distinct OS rates. Multivariate Cox analysis suggested that the ten-CD4+ MTRG signature was an independent risk factor in GC. A nomogram incorporating this signature and clinical variables was established, and the C-index was 0.73 (95% CI: 0.697–0.763). Calibration curves and DCA presented high credibility for the OS nomogram.ConclusionWe identified three molecule subtypes, ten CD4+ MTRGs, and generated a prognostic nomogram that reliably predicts OS in GC. These findings have implications for precise prognosis prediction and individualized targeted therapy.